Long Wavelength Infrared (LWIR) images have many uses in industry, military, medicine, and science. For example, nondestructive testing uses thermal imagers for detecting defect locations in manufactured materials, thereby allowing for better quality control. Unmanned Airborne Vehicles (UAV) and security cameras often couple a thermal imager with a visible light (VL) camera to enhance night vision for scouting and to improve automatic threat detection over large distances. Firefighters carry handheld imagers while scouting for critical burn points in burning buildings and possible thermal hazards. Thermographers use high-resolution thermal imagers for detecting inflammation, irregular blood-flow, and tumors.
Natural Scene Statistic (NSS) models describe statistical regularities that are observed on images taken of the natural world. Examples of NSS of visible light images include the 1/f behavior of the amplitude spectrum, the sparse coding characteristic of visual cortical filters in response to natural image stimuli, and the Gaussianity exhibited by visual signals following band-pass filter and adaptive gain control operations. Early cortical processing in higher mammalian visual systems appears to have adapted to these natural statistics, and much research into biological visual functioning has been guided by the “efficient coding” hypothesis, which assumes that visual neurons have adapted to efficiently encode natural visual stimuli.
Given their widespread use and application, LWIR images have been well studied. Sources of spatial noise and the effect of noise on minimum resolvable temperature differences (MTD) have been characterized as a function of frequency. Spatial noise has been further characterized in infrared (IR) images by using Principle Components Analysis (PCA) to separate spatial and temporal noise from a sequence of frames. Nonuniformity (NU) noise common in LWIR images expressed in the frequency domain has been modeled as distinct from independent spatial noise.
Although natural scene statistics have proven to be highly successful tools in applications on visible light images, the development and use of similar models has not been nearly as widespread on LWIR images. However, the statistics of visible-light and LWIR are predictably different. To measure NU, noise, blur, and changes in brightness, four Image Quality Indicators (IQIs) have been developed. To measure NU in LWIR images, a Roughness Index has been introduced to compute using discrete spatial derivatives. An improved index, the Effective Roughness Index, measures this roughness index using a high-pass image.
However, currently existing technologies, such as the signal-to-noise ratio, roughness index and the effective roughness index, do not provide an accurate and precise measurement of non-uniformity noise in an infrared image or video.